Glass Type Classification with Machine Learning

Glass is a fully recyclable and sustainable material used in the fabrication of everything from tableware or windows for the automotive and the construction industries to more technical glass, such as flat screens for smartphones. Demand for glass is on the rise due to population and infrastructure growth. Glass is made in furnaces at very high temperatures with a process that requires constant improvement to meet the energy efficiency and environmental challenges posed by the manufacturing cycle.

Enhancing efficiency with oxygen Fuel technology The glass industry is under severe cost constraints and increasing environmental pressure to reduce emissions. Air Liquide provides glass manufacturers with solutions to improve their competitiveness and environmental footprint.

As long standing experts in oxy-combustion, which is the process of burning a fuel using pure oxygen instead of air as the primary oxidant, we have substantial R&D resources and a strong experience in glass making

Glass Production Process Steps:

Glass melting

Before glass can even be formed, raw materials, mainly sand, and recycled glass must be melted at extremely high temperatures (1,400°C). To achieve the heat intensity required in the glass furnaces, Air Liquide steps in with oxy-combustion technologies that replace air with oxygen, thus improving the melting process, reducing air pollutant emissions and saving fuel. Our Nexelia™ all-in-one solutions allow for rapid alternation of melting temperatures, for operational flexibility and for automatic temperature control in a wide range of glass furnace sizes.

Glass polishing

Before a finished product can be packaged and put on the market, defaults must be removed. Air Liquide supplies oxy-natural based gas and oxy-hydrogen based combustion technologies to polish the product surface and remove the defaults present in glass. This gives products, including fine items such as tableware, perfume bottles and crystal glasses, a pleasing, smooth and shiny appearance.

Insulated glass

Air Liquide provides glass insulation solutions that involve injecting rare gases such as argon, xenon and krypton into multiple-glazed windows. Not only does this improve building acoustics, it also substantially increases energy efficiency, helping protect the environment

Attribute Information:

1. Id number: 1 to 214 (removed from CSV file)
2. RI: refractive index
3. Na: Sodium (unit measurement: weight percent in corresponding oxide, as are attributes 4-10)
4. Mg: Magnesium
5. Al: Aluminum
6. Si: Silicon
7. K: Potassium
8. Ca: Calcium
9. Ba: Barium
10. Fe: Iron
11. Type of glass: (class attribute)
    -- 1 buildingwindowsfloatprocessed
    -- 2 buildingwindowsnonfloatprocessed
    -- 3 vehiclewindowsfloatprocessed
    -- 4 vehiclewindowsnonfloatprocessed (none in this database)
    -- 5 containers
    -- 6 tableware
    -- 7 headlamps

Importing the Libraries

Contents

1. Loading the Libraries

2. Loading the Dataset in to Python environment

3. Summarize the data

4. Exploratory Data Analysis

5. Evaluation of Algorithms

6. Algorithm Tuning

7. Diagnose the performance of the best Algorithms

8. Multiple Models Machine Learning

1. Logistic Regression
2. K Nearest Neighbor Classifier
3. Support Vector Machine (Linear Classifier)
4. Support Vector Machine (RBF Classifier)
5. Gaussian Naive Bayes Classifier
6. Decision Tree Classifier
7. Random Forest Classifier
8. Xgboost Classifier
9. SGD Classifier
10. AdaBoost Classifier
11. Gradient Boosting Classifier
12. Quadratic Discriminant Analysis

Importing the required Libraries

import pandas as pd
import numpy as np

# Visualization
import matplotlib.pyplot as plt
import seaborn as sns
import sys
import pandas_profiling as pp

# Data Transformation
from sklearn.base import TransformerMixin
from scipy.stats import boxcox

#Preprocessing
from sklearn.preprocessing import FunctionTransformer, StandardScaler

# Dimentionality Reduction
from sklearn.decomposition import PCA
from sklearn. discriminant_analysis import LinearDiscriminantAnalysis as LDA

# Train Test Split Model Selection
from sklearn.model_selection import train_test_split, KFold, StratifiedKFold, cross_val_score, GridSearchCV, learning_curve, validation_curve

# Streaming Pipelines
from sklearn.pipeline import Pipeline

# Boxcox transformation class
from sklearn.base import BaseEstimator, TransformerMixin
from collections import Counter
import warnings

# Models
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from xgboost import XGBClassifier, plot_importance
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, ExtraTreesClassifier, GradientBoostingClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from time import time

%matplotlib inline

warnings.filterwarnings('ignore')

sns.set_style('whitegrid')

data = pd.read_csv("C:\\Users\\SHASHI\\OneDrive\\Desktop\\Python\\glass.csv")

data.head(5)

	RI	Na	Mg	Al	Si	K	Ca	Ba	Fe	Type
0	1.52101	13.64	4.49	1.10	71.78	0.06	8.75	0.0	0.0	1
1	1.51761	13.89	3.60	1.36	72.73	0.48	7.83	0.0	0.0	1
2	1.51618	13.53	3.55	1.54	72.99	0.39	7.78	0.0	0.0	1
3	1.51766	13.21	3.69	1.29	72.61	0.57	8.22	0.0	0.0	1
4	1.51742	13.27	3.62	1.24	73.08	0.55	8.07	0.0	0.0	1

data.shape

(214, 10)

data.dtypes

RI      float64
Na      float64
Mg      float64
Al      float64
Si      float64
K       float64
Ca      float64
Ba      float64
Fe      float64
Type      int64
dtype: object

Summarization of the data

Descriptive Statistics

data.describe()

	RI	Na	Mg	Al	Si	K	Ca	Ba	Fe	Type
count	214.000000	214.000000	214.000000	214.000000	214.000000	214.000000	214.000000	214.000000	214.000000	214.000000
mean	1.518365	13.407850	2.684533	1.444907	72.650935	0.497056	8.956963	0.175047	0.057009	2.780374
std	0.003037	0.816604	1.442408	0.499270	0.774546	0.652192	1.423153	0.497219	0.097439	2.103739
min	1.511150	10.730000	0.000000	0.290000	69.810000	0.000000	5.430000	0.000000	0.000000	1.000000
25%	1.516523	12.907500	2.115000	1.190000	72.280000	0.122500	8.240000	0.000000	0.000000	1.000000
50%	1.517680	13.300000	3.480000	1.360000	72.790000	0.555000	8.600000	0.000000	0.000000	2.000000
75%	1.519157	13.825000	3.600000	1.630000	73.087500	0.610000	9.172500	0.000000	0.100000	3.000000
max	1.533930	17.380000	4.490000	3.500000	75.410000	6.210000	16.190000	3.150000	0.510000	7.000000

Finding the Missing Values

data.isnull().sum()

RI      0
Na      0
Mg      0
Al      0
Si      0
K       0
Ca      0
Ba      0
Fe      0
Type    0
dtype: int64

data['Type'].value_counts()

2    76
1    70
7    29
3    17
5    13
6     9
Name: Type, dtype: int64

data['Type'].value_counts().sort_index(ascending=True)

1    70
2    76
3    17
5    13
6     9
7    29
Name: Type, dtype: int64

plt.figure(figsize=(16,8))
sns.countplot(x='Type', data=data, order=data['Type'].value_counts().index);

Finding Outliers

plt.figure(figsize=(20,10))
sns.boxplot(data=data, orient="h")

<AxesSubplot:>

Pearson Correlation

plt.figure(figsize=(20,10))
sns.heatmap(data.corr(method='pearson'), cbar=False, annot=True, fmt='.1f', linewidths=0.20, cmap='Set1')

<AxesSubplot:>

pp.ProfileReport(data)

data['Type'].value_counts()
data['Type'].value_counts()*100/len(data)
sns.countplot(x='Type', data=data, palette='dark')

<AxesSubplot:xlabel='Type', ylabel='count'>

Train Test and Split the data

X = data.drop('Type',axis=1)
y = data['Type']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=0)

from imblearn.over_sampling import SMOTE

sm = SMOTE(random_state=27)

print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)

(171, 9)
(43, 9)
(171,)
(43,)

Scaling the dataset

sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

Data is ready now for Model Building

def models(X_train,y_train):
  
  #Using Logistic Regression Algorithm to the Training Set
  from sklearn.linear_model import LogisticRegression
  log = LogisticRegression(random_state = 0)
  log.fit(X_train, y_train)
  
  #Using KNeighborsClassifier Method of neighbors class to use Nearest Neighbor algorithm
  from sklearn.neighbors import KNeighborsClassifier
  knn = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
  knn.fit(X_train, y_train)
    
  #Using SVC method of svm class to use Support Vector Machine Algorithm
  from sklearn.svm import SVC
  svc_lin = SVC(kernel = 'linear', random_state =0)
  svc_lin.fit(X_train, y_train)

  #Using SVC method of svm class to use Kernel SVM Algorithm
  from sklearn.svm import SVC
  svc_rbf = SVC(kernel = 'rbf', random_state = 0)
  svc_rbf.fit(X_train, y_train)

  #Using GaussianNB method of naïve_bayes class to use Naïve Bayes Algorithm
  from sklearn.naive_bayes import GaussianNB
  gauss = GaussianNB()
  gauss.fit(X_train, y_train)

 

  #Using RandomForestClassifier method of ensemble class to use Random Forest Classification algorithm
  from sklearn.ensemble import RandomForestClassifier
  forest = RandomForestClassifier(n_estimators = 100, criterion = 'entropy', random_state = 0)
  forest.fit(X_train, y_train)
  
    
  #Using DecisionTreeClassifier of tree class to use Decision Tree Algorithm
  from sklearn.tree import DecisionTreeClassifier
  tree = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)
  tree.fit(X_train, y_train)
    
    
    
    
 #Using xgboostClassifier of tree class to use Decision Tree Algorithm
  from xgboost import XGBClassifier 
  xgboost = XGBClassifier(max_depth=5, learning_rate=0.01, n_estimators=100, gamma=0, 
                        min_child_weight=1, subsample=0.8, colsample_bytree=0.8, reg_alpha=0.005)
  xgboost.fit(X_train, y_train)
    
    
    
 #Using  SGDClassifierr of tree class to use Decision Tree Algorithm    
  from sklearn.linear_model import SGDClassifier
  SGD = SGDClassifier()
  SGD.fit(X_train, y_train)
    
    
  #Using  AdaBoostClassifier of tree class to use Decision Tree Algorithm    
  from sklearn.ensemble import AdaBoostClassifier
  Ada = AdaBoostClassifier(n_estimators=2000, random_state = 0)
  Ada.fit(X_train, y_train)
   



  #Using  GradientBoostingClassifier of tree class to use Decision Tree Algorithm    
  from sklearn.ensemble import GradientBoostingClassifier
  clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1)
  clf.fit(X_train, y_train) 

#####
  

 #Using Quadratic Discriminant Analysis of tree class to use Decision Tree Algorithm    
  from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis 
  QDA = QuadraticDiscriminantAnalysis ()
  QDA.fit(X_train, y_train)
    

  #print model accuracy on the training data.
  print('[0]Logistic Regression Training Accuracy:', log.score(X_train, y_train)*100)
  print('[1]K Nearest Neighbor Training Accuracy:', knn.score(X_train, y_train)*100)
  print('[2]Support Vector Machine (Linear Classifier) Training Accuracy:', svc_lin.score(X_train, y_train)*100)
  print('[3]Support Vector Machine (RBF Classifier) Training Accuracy:', svc_rbf.score(X_train, y_train)*100)
  print('[4]Gaussian Naive Bayes Training Accuracy:', gauss.score(X_train, y_train)*100)
  print('[5]Decision Tree Classifier Training Accuracy:', tree.score(X_train, y_train)*100)
  print('[6]Random Forest Classifier Training Accuracy:', forest.score(X_train, y_train)*100)
  print('[7]Xgboost Classifier Training Accuracy:', xgboost.score(X_train, y_train)*100)
  print('[8]SGD Classifier Training Accuracy:', SGD.score(X_train, y_train)*100)
  print('[9]AdaBoost Classifier Training Accuracy:', Ada.score(X_train, y_train)*100)
  print('[10]GradientBoosting Classifier Training Accuracy:', clf.score(X_train, y_train)*100)
  print('[11]Quadratic Discriminant AnalysisTraining Accuracy:', QDA.score(X_train, y_train)*100)

  return log, knn, svc_lin, svc_rbf, gauss,tree,forest,xgboost,SGD,Ada,clf,QDA

model = models(X_train,y_train)

[20:42:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[0]Logistic Regression Training Accuracy: 59.06432748538012
[1]K Nearest Neighbor Training Accuracy: 72.51461988304094
[2]Support Vector Machine (Linear Classifier) Training Accuracy: 60.23391812865497
[3]Support Vector Machine (RBF Classifier) Training Accuracy: 67.2514619883041
[4]Gaussian Naive Bayes Training Accuracy: 50.877192982456144
[5]Decision Tree Classifier Training Accuracy: 100.0
[6]Random Forest Classifier Training Accuracy: 100.0
[7]Xgboost Classifier Training Accuracy: 81.28654970760235
[8]SGD Classifier Training Accuracy: 46.783625730994146
[9]AdaBoost Classifier Training Accuracy: 57.89473684210527
[10]GradientBoosting Classifier Training Accuracy: 74.85380116959064
[11]Quadratic Discriminant AnalysisTraining Accuracy: 57.89473684210527

Lets showcase the confusion matrix and accuracy for the test data

from sklearn.metrics import confusion_matrix
for i in range(len(model)):
  cm = confusion_matrix(y_test, model[i].predict(X_test))
  TN = cm[0][0]
  TP = cm[1][1]
  FN = cm[1][0]
  FP = cm[0][1]
  print(cm)
  print('Model[{}] Testing Accuracy = "{}!"'.format(i,  (TP + TN) / (TP + TN + FN + FP)))
  print()# Print a new line

#Show other ways to get the classification accuracy & other metrics 

from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score

for i in range(len(model)):
  print('Model ',i)
  #Check precision, recall, f1-score
  print( classification_report(y_test, model[i].predict(X_test)) )
  #Another way to get the models accuracy on the test data
  print( accuracy_score(y_test, model[i].predict(X_test)))
  print()#Print a new line

[[ 8  1  0  0  0  0]
 [13  5  0  0  0  1]
 [ 2  3  0  0  0  0]
 [ 0  2  0  0  0  0]
 [ 0  2  0  0  0  0]
 [ 0  0  0  0  0  6]]
Model[0] Testing Accuracy = "0.48148148148148145!"

[[ 7  2  0  0  0  0]
 [ 8 11  0  0  0  0]
 [ 3  2  0  0  0  0]
 [ 0  1  0  0  1  0]
 [ 0  1  0  0  1  0]
 [ 0  0  0  0  0  6]]
Model[1] Testing Accuracy = "0.6428571428571429!"

[[ 8  1  0  0  0  0]
 [12  6  0  1  0  0]
 [ 2  3  0  0  0  0]
 [ 0  1  0  1  0  0]
 [ 0  1  0  0  0  1]
 [ 0  0  0  1  0  5]]
Model[2] Testing Accuracy = "0.5185185185185185!"

[[ 9  0  0  0  0  0]
 [ 9 10  0  0  0  0]
 [ 4  1  0  0  0  0]
 [ 0  1  0  0  1  0]
 [ 0  2  0  0  0  0]
 [ 0  0  0  0  1  5]]
Model[3] Testing Accuracy = "0.6785714285714286!"

[[ 8  1  0  0  0  0]
 [17  1  0  1  0  0]
 [ 4  1  0  0  0  0]
 [ 0  2  0  0  0  0]
 [ 0  2  0  0  0  0]
 [ 0  0  0  0  0  6]]
Model[4] Testing Accuracy = "0.3333333333333333!"

[[ 9  0  0  0  0  0]
 [ 7 11  1  0  0  0]
 [ 2  2  1  0  0  0]
 [ 0  0  0  1  1  0]
 [ 0  0  0  1  0  1]
 [ 0  0  0  0  1  5]]
Model[5] Testing Accuracy = "0.7407407407407407!"

[[ 9  0  0  0  0  0]
 [ 6 13  0  0  0  0]
 [ 3  2  0  0  0  0]
 [ 0  1  0  0  1  0]
 [ 0  0  0  1  0  1]
 [ 0  0  0  0  1  5]]
Model[6] Testing Accuracy = "0.7857142857142857!"

[[ 8  1  0  0  0  0]
 [ 3 16  0  0  0  0]
 [ 4  1  0  0  0  0]
 [ 0  2  0  0  0  0]
 [ 1  0  0  0  0  1]
 [ 0  1  0  0  0  5]]
Model[7] Testing Accuracy = "0.8571428571428571!"

[[ 0  9  0  0  0  0]
 [ 1 17  0  0  0  1]
 [ 1  4  0  0  0  0]
 [ 0  1  0  0  0  1]
 [ 0  1  0  0  0  1]
 [ 0  0  0  0  0  6]]
Model[8] Testing Accuracy = "0.6296296296296297!"

[[ 9  0  0  0  0  0]
 [17  2  0  0  0  0]
 [ 5  0  0  0  0  0]
 [ 0  0  0  2  0  0]
 [ 0  0  0  1  0  1]
 [ 0  0  0  1  0  5]]
Model[9] Testing Accuracy = "0.39285714285714285!"

[[ 7  0  2  0  0  0]
 [ 7 10  0  2  0  0]
 [ 3  0  2  0  0  0]
 [ 0  0  0  2  0  0]
 [ 0  0  0  1  0  1]
 [ 0  0  0  2  0  4]]
Model[10] Testing Accuracy = "0.7083333333333334!"

[[ 9  0  0  0  0  0]
 [17  1  0  1  0  0]
 [ 5  0  0  0  0  0]
 [ 0  0  0  1  1  0]
 [ 0  1  0  0  0  1]
 [ 0  0  0  0  0  6]]
Model[11] Testing Accuracy = "0.37037037037037035!"

Model  0
              precision    recall  f1-score   support

           1       0.35      0.89      0.50         9
           2       0.38      0.26      0.31        19
           3       0.00      0.00      0.00         5
           5       0.00      0.00      0.00         2
           6       0.00      0.00      0.00         2
           7       0.86      1.00      0.92         6

    accuracy                           0.44        43
   macro avg       0.26      0.36      0.29        43
weighted avg       0.36      0.44      0.37        43

0.4418604651162791

Model  1
              precision    recall  f1-score   support

           1       0.39      0.78      0.52         9
           2       0.65      0.58      0.61        19
           3       0.00      0.00      0.00         5
           5       0.00      0.00      0.00         2
           6       0.50      0.50      0.50         2
           7       1.00      1.00      1.00         6

    accuracy                           0.58        43
   macro avg       0.42      0.48      0.44        43
weighted avg       0.53      0.58      0.54        43

0.5813953488372093

Model  2
              precision    recall  f1-score   support

           1       0.36      0.89      0.52         9
           2       0.50      0.32      0.39        19
           3       0.00      0.00      0.00         5
           5       0.33      0.50      0.40         2
           6       0.00      0.00      0.00         2
           7       0.83      0.83      0.83         6

    accuracy                           0.47        43
   macro avg       0.34      0.42      0.36        43
weighted avg       0.43      0.47      0.41        43

0.46511627906976744

Model  3
              precision    recall  f1-score   support

           1       0.41      1.00      0.58         9
           2       0.71      0.53      0.61        19
           3       0.00      0.00      0.00         5
           5       0.00      0.00      0.00         2
           6       0.00      0.00      0.00         2
           7       1.00      0.83      0.91         6

    accuracy                           0.56        43
   macro avg       0.35      0.39      0.35        43
weighted avg       0.54      0.56      0.52        43

0.5581395348837209

Model  4
              precision    recall  f1-score   support

           1       0.28      0.89      0.42         9
           2       0.14      0.05      0.08        19
           3       0.00      0.00      0.00         5
           5       0.00      0.00      0.00         2
           6       0.00      0.00      0.00         2
           7       1.00      1.00      1.00         6

    accuracy                           0.35        43
   macro avg       0.24      0.32      0.25        43
weighted avg       0.26      0.35      0.26        43

0.3488372093023256

Model  5
              precision    recall  f1-score   support

           1       0.50      1.00      0.67         9
           2       0.85      0.58      0.69        19
           3       0.50      0.20      0.29         5
           5       0.50      0.50      0.50         2
           6       0.00      0.00      0.00         2
           7       0.83      0.83      0.83         6

    accuracy                           0.63        43
   macro avg       0.53      0.52      0.50        43
weighted avg       0.68      0.63      0.62        43

0.627906976744186

Model  6
              precision    recall  f1-score   support

           1       0.50      1.00      0.67         9
           2       0.81      0.68      0.74        19
           3       0.00      0.00      0.00         5
           5       0.00      0.00      0.00         2
           6       0.00      0.00      0.00         2
           7       0.83      0.83      0.83         6

    accuracy                           0.63        43
   macro avg       0.36      0.42      0.37        43
weighted avg       0.58      0.63      0.58        43

0.627906976744186

Model  7
              precision    recall  f1-score   support

           1       0.50      0.89      0.64         9
           2       0.76      0.84      0.80        19
           3       0.00      0.00      0.00         5
           5       0.00      0.00      0.00         2
           6       0.00      0.00      0.00         2
           7       0.83      0.83      0.83         6

    accuracy                           0.67        43
   macro avg       0.35      0.43      0.38        43
weighted avg       0.56      0.67      0.60        43

0.6744186046511628

Model  8
              precision    recall  f1-score   support

           1       0.00      0.00      0.00         9
           2       0.53      0.89      0.67        19
           3       0.00      0.00      0.00         5
           5       0.00      0.00      0.00         2
           6       0.00      0.00      0.00         2
           7       0.67      1.00      0.80         6

    accuracy                           0.53        43
   macro avg       0.20      0.32      0.24        43
weighted avg       0.33      0.53      0.41        43

0.5348837209302325

Model  9
              precision    recall  f1-score   support

           1       0.29      1.00      0.45         9
           2       1.00      0.11      0.19        19
           3       0.00      0.00      0.00         5
           5       0.50      1.00      0.67         2
           6       0.00      0.00      0.00         2
           7       0.83      0.83      0.83         6

    accuracy                           0.42        43
   macro avg       0.44      0.49      0.36        43
weighted avg       0.64      0.42      0.33        43

0.4186046511627907

Model  10
              precision    recall  f1-score   support

           1       0.41      0.78      0.54         9
           2       1.00      0.53      0.69        19
           3       0.50      0.40      0.44         5
           5       0.29      1.00      0.44         2
           6       0.00      0.00      0.00         2
           7       0.80      0.67      0.73         6

    accuracy                           0.58        43
   macro avg       0.50      0.56      0.47        43
weighted avg       0.71      0.58      0.59        43

0.5813953488372093

Model  11
              precision    recall  f1-score   support

           1       0.29      1.00      0.45         9
           2       0.50      0.05      0.10        19
           3       0.00      0.00      0.00         5
           5       0.50      0.50      0.50         2
           6       0.00      0.00      0.00         2
           7       0.86      1.00      0.92         6

    accuracy                           0.40        43
   macro avg       0.36      0.43      0.33        43
weighted avg       0.42      0.40      0.29        43

0.3953488372093023

acc_1 = 0.703703*100 
acc_2 = 0.685185*100
acc_3 = 0.666666*100
acc_4 = 0.759259*100
acc_5 = 0.537037*100
acc_6 = 0.648148*100
acc_7 = 0.796296*100
acc_8 = 0.814814*100
acc_9 = 0.592592*100
acc_10 = 0.61111*100
acc_11 = 0.81481*100
acc_12 = 0.64814*100
results = pd.DataFrame([["Logistic Regression",acc_1],["Nearest Neighbor",acc_2],["Support Vector Machine (Linear Classifier)",acc_3],
                       ["Support Vector Machine (RBF Classifier)",acc_4],["Gaussian Naive Bayes",acc_5],["Decision Tree Classifier",acc_6],
                       ["Random Forest Classifier",acc_7],["Xgboost Classifier",acc_8],["SGD Classifier ",acc_9],["AdaBoost Classifier",acc_10],
                        ["GradientBoosting Classifier",acc_11],["Quadratic Discriminant Analysis",acc_12],
                       ],columns = ["Models","Accuracy Score"]).sort_values(by='Accuracy Score',ascending=False)
results.style.background_gradient(cmap='Blues')

	Models	Accuracy Score
7	Xgboost Classifier	81.481400
10	GradientBoosting Classifier	81.481000
6	Random Forest Classifier	79.629600
3	Support Vector Machine (RBF Classifier)	75.925900
0	Logistic Regression	70.370300
1	Nearest Neighbor	68.518500
2	Support Vector Machine (Linear Classifier)	66.666600
5	Decision Tree Classifier	64.814800
11	Quadratic Discriminant Analysis	64.814000
9	AdaBoost Classifier	61.111000
8	SGD Classifier	59.259200
4	Gaussian Naive Bayes	53.703700